An efficient approach to model updating for a multispan railway bridge using orthogonal diagonalization combined with improved particle swarm optimization

Tran-Ngoc, H.; He, Leqia; Reynders, Edwin; Khatir, Samir; Le-Xuan, T.; Roeck, Guido De; Bui-Tien, Thanh; Wahab, Magd Abdel

doi:10.1016/j.jsv.2020.115315

Cited by 61 publications

(22 citation statements)

References 38 publications

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“…After that, an optimization-based model updating technique is used to minimize the difference between modal characteristics of cables with the numerical counterpart, including eigenfrequencies and mode shapes. As detailed solutions for similar problems can be found in the optimization literature [33], [34], thus for brevity, one only presents measured forces in cables in Table V.…”

Section: B Case Study 2: My Thuan Stayed-bridgementioning

confidence: 99%

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Dang

Tran-Ngoc

Nguyen

et al. 2021

IEEE Trans. Automat. Sci. Eng.

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Section: B Case Study 2: My Thuan Stayed-bridgementioning

confidence: 99%

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Dang

Tran-Ngoc

Nguyen

et al. 2021

IEEE Trans. Automat. Sci. Eng.

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“…7, and at this time the population is in the global search stage; When 1 | E | ≤ , the agents move according to Eq. (10), (11), (14) and (16). At this time, the population can choose a variety of movement methods, which expands the movement range of the agents in a certain local area and increases exploitation of the agents.…”

Section: B Henry Gas Solubility Optimization Algorithm Based On Harrmentioning

confidence: 99%

“…When HHO-HGSO is executed, all agents will select a position update strategy based on Eq. (7), (10), (11), (14), (16) in each iteration. The complexity of these location update strategies are the same, so the time complexity of HHO-HGSO will not increase, which is still o(obj) * d) * n * o(t .…”

Section: ) Time Complexitymentioning

confidence: 99%

“…The most classic algorithm is particle swarm optimization (PSO) algorithm [11]. The original PSO did not have strong exploration and exploitation, many scholars improved it and enhanced its optimization ability [12][13][14][15]. After continuous exploration by researchers, the swarm-based algorithm has developed rapidly, Ant colony algorithm is also a common swarm-based algorithm [16], which is often used to solve path planning problems [17][18] and traveling salesman problems [19][20].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization

et al. 2020

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Henry Gas Solubility Optimization Algorithm (HGSO) is a physical-based algorithm based on Henry's Law and simulates the process that the solubility of gas in liquid changes with temperature. The search strategy of the HGSO is very simple, which results in the algorithm having poor exploitation ability and unable to find a more accurate optimal solution. The Harris Hawk optimization algorithm (HHO) has strong exploitation because of its diverse exploitation strategies. In order to make up for the shortcomings of HGSO algorithm, this paper proposes an improved Henry gas solubility optimization algorithm (HHO-HGSO) based on the Harris Hawk optimization. During the iteration, taking the escaping energy function of the HHO algorithm as an indicator and determining a threshold, when the escaping energy function value is greater than the threshold, the algorithm conducts the search strategy of the HGSO algorithm, when less than the threshold, executes four exploitation strategies of Harris Hawk. In order to verify the performance of the proposed algorithm, HHO-HGSO is used to optimize CEC2005 and CEC2017 benchmark test functions and solve 4 real engineering design problems. Marine predators algorithm (MPA), Whale optimization algorithm (WOA), Lightning search algorithm (LSA), Water cycle algorithm (WCA), HGSO, and HHO were used to conduct comparative experiments. The simulation results show that the proposed HHO-HGSO algorithm has strong optimization ability. The core code of the paper has been uploaded in https://github.com/xwaiyy123/HHO-HGSO.

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“…rough adopting lots of benchmarks with different characteristics in the experiments, it is proved that PSOCO is a competitive PSO variant for some benchmark functions. Tran-Ngoc et al [47] improved the calculation method of PSO's inertia weight factor by introducing contraction factor K to improve the disadvantage of premature convergence of PSO. en, the best local position of particles in PSO based on orthogonal diagonalization (OD) was arranged, which greatly reduced the calculation cost of PSO.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm

Guo

Guan

Zhao

2020

Advances in Civil Engineering

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A method based on the wavelet transform and improved particle swarm optimization (WIPSO) algorithm is proposed to identify the microdamage of structures. First, the singularity of wavelet coefficients is used to identify the structural damage location, and then, the improved particle swarm optimization (IPSO) algorithm is used to calculate the optimal solution of the objective function of the structural damage location to determine the structural damage severity. To study the performance of WIPSO, the structural microdamage severity is set within 10%, and a numerical simulation and experimental structure under different damage scenarios are considered. In addition, the ability of wavelet coefficients to identify the location of the structural damage under different noise levels is studied. To evaluate the performance of IPSO, the standard particle swarm optimization algorithm with an inertia weight factor of 0.8 (0.8PSO), the genetic algorithm (GA), and the bat algorithm (BA) are also considered. The results show that WIPSO can effectively and accurately identify the structural damage location and severity. Wavelet transform is very robust to the structural damage location. Compared with the standard 0.8PSO and other mainstream algorithms, IPSO has good convergence and performs more stable and more accurate in the identification of structural damage severity.

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An efficient approach to model updating for a multispan railway bridge using orthogonal diagonalization combined with improved particle swarm optimization

Cited by 61 publications

References 38 publications

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization

Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm

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